from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-21 14:05:47.019199
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 21, Nov, 2020
Time: 14:05:51
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.4032
Nobs: 117.000 HQIC: -43.6653
Log likelihood: 1200.74 FPE: 4.60683e-20
AIC: -44.5279 Det(Omega_mle): 2.20212e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.722981 0.214441 3.371 0.001
L1.Burgenland 0.138226 0.092331 1.497 0.134
L1.Kärnten -0.310798 0.077483 -4.011 0.000
L1.Niederösterreich 0.000976 0.224397 0.004 0.997
L1.Oberösterreich 0.270620 0.181328 1.492 0.136
L1.Salzburg 0.131665 0.091358 1.441 0.150
L1.Steiermark 0.079082 0.129567 0.610 0.542
L1.Tirol 0.159416 0.085342 1.868 0.062
L1.Vorarlberg 0.015855 0.084746 0.187 0.852
L1.Wien -0.171982 0.175428 -0.980 0.327
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.738687 0.275501 2.681 0.007
L1.Burgenland -0.021589 0.118621 -0.182 0.856
L1.Kärnten 0.349011 0.099546 3.506 0.000
L1.Niederösterreich 0.059480 0.288292 0.206 0.837
L1.Oberösterreich -0.213969 0.232960 -0.918 0.358
L1.Salzburg 0.164785 0.117372 1.404 0.160
L1.Steiermark 0.192849 0.166460 1.159 0.247
L1.Tirol 0.137232 0.109643 1.252 0.211
L1.Vorarlberg 0.192748 0.108877 1.770 0.077
L1.Wien -0.567290 0.225380 -2.517 0.012
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.372929 0.090934 4.101 0.000
L1.Burgenland 0.101021 0.039153 2.580 0.010
L1.Kärnten -0.027560 0.032857 -0.839 0.402
L1.Niederösterreich 0.127809 0.095156 1.343 0.179
L1.Oberösterreich 0.264294 0.076893 3.437 0.001
L1.Salzburg -0.002926 0.038741 -0.076 0.940
L1.Steiermark -0.066751 0.054943 -1.215 0.224
L1.Tirol 0.097604 0.036190 2.697 0.007
L1.Vorarlberg 0.143579 0.035937 3.995 0.000
L1.Wien 0.001488 0.074391 0.020 0.984
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.218342 0.109102 2.001 0.045
L1.Burgenland 0.003178 0.046975 0.068 0.946
L1.Kärnten 0.035523 0.039421 0.901 0.368
L1.Niederösterreich 0.090190 0.114167 0.790 0.430
L1.Oberösterreich 0.348235 0.092255 3.775 0.000
L1.Salzburg 0.093028 0.046481 2.001 0.045
L1.Steiermark 0.192633 0.065920 2.922 0.003
L1.Tirol 0.026783 0.043420 0.617 0.537
L1.Vorarlberg 0.112383 0.043117 2.606 0.009
L1.Wien -0.119756 0.089253 -1.342 0.180
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.903570 0.234497 3.853 0.000
L1.Burgenland 0.033645 0.100966 0.333 0.739
L1.Kärnten -0.013441 0.084730 -0.159 0.874
L1.Niederösterreich -0.141635 0.245384 -0.577 0.564
L1.Oberösterreich 0.046068 0.198287 0.232 0.816
L1.Salzburg 0.056022 0.099903 0.561 0.575
L1.Steiermark 0.113161 0.141685 0.799 0.424
L1.Tirol 0.238208 0.093324 2.552 0.011
L1.Vorarlberg 0.025249 0.092672 0.272 0.785
L1.Wien -0.225559 0.191836 -1.176 0.240
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.188900 0.163228 1.157 0.247
L1.Burgenland -0.040337 0.070280 -0.574 0.566
L1.Kärnten -0.009458 0.058979 -0.160 0.873
L1.Niederösterreich 0.204337 0.170806 1.196 0.232
L1.Oberösterreich 0.395153 0.138023 2.863 0.004
L1.Salzburg -0.036170 0.069540 -0.520 0.603
L1.Steiermark -0.055635 0.098624 -0.564 0.573
L1.Tirol 0.195338 0.064961 3.007 0.003
L1.Vorarlberg 0.056234 0.064507 0.872 0.383
L1.Wien 0.112313 0.133532 0.841 0.400
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.340007 0.207680 1.637 0.102
L1.Burgenland 0.063254 0.089420 0.707 0.479
L1.Kärnten -0.078432 0.075041 -1.045 0.296
L1.Niederösterreich -0.151286 0.217322 -0.696 0.486
L1.Oberösterreich -0.122674 0.175611 -0.699 0.485
L1.Salzburg -0.000039 0.088478 -0.000 1.000
L1.Steiermark 0.382446 0.125482 3.048 0.002
L1.Tirol 0.536199 0.082652 6.487 0.000
L1.Vorarlberg 0.227704 0.082075 2.774 0.006
L1.Wien -0.185318 0.169898 -1.091 0.275
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.225541 0.237827 0.948 0.343
L1.Burgenland 0.006838 0.102400 0.067 0.947
L1.Kärnten -0.071452 0.085933 -0.831 0.406
L1.Niederösterreich 0.209111 0.248869 0.840 0.401
L1.Oberösterreich 0.011957 0.201103 0.059 0.953
L1.Salzburg 0.233998 0.101321 2.309 0.021
L1.Steiermark 0.154820 0.143697 1.077 0.281
L1.Tirol 0.054344 0.094649 0.574 0.566
L1.Vorarlberg -0.001704 0.093989 -0.018 0.986
L1.Wien 0.194945 0.194560 1.002 0.316
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.692770 0.131305 5.276 0.000
L1.Burgenland -0.010136 0.056535 -0.179 0.858
L1.Kärnten -0.012107 0.047444 -0.255 0.799
L1.Niederösterreich -0.078102 0.137401 -0.568 0.570
L1.Oberösterreich 0.266807 0.111030 2.403 0.016
L1.Salzburg 0.005496 0.055940 0.098 0.922
L1.Steiermark 0.004825 0.079336 0.061 0.952
L1.Tirol 0.077290 0.052256 1.479 0.139
L1.Vorarlberg 0.183886 0.051891 3.544 0.000
L1.Wien -0.116725 0.107417 -1.087 0.277
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.085348 -0.086631 0.188194 0.242352 0.015906 0.068949 -0.148276 0.084566
Kärnten 0.085348 1.000000 -0.079681 0.172742 0.054899 -0.164060 0.169543 0.003812 0.277854
Niederösterreich -0.086631 -0.079681 1.000000 0.211108 0.027227 0.143297 0.055952 0.027021 0.342300
Oberösterreich 0.188194 0.172742 0.211108 1.000000 0.231794 0.261816 0.063379 0.048045 0.022528
Salzburg 0.242352 0.054899 0.027227 0.231794 1.000000 0.135152 0.029793 0.057317 -0.080791
Steiermark 0.015906 -0.164060 0.143297 0.261816 0.135152 1.000000 0.096149 0.094396 -0.206459
Tirol 0.068949 0.169543 0.055952 0.063379 0.029793 0.096149 1.000000 0.126611 0.082864
Vorarlberg -0.148276 0.003812 0.027021 0.048045 0.057317 0.094396 0.126611 1.000000 0.059575
Wien 0.084566 0.277854 0.342300 0.022528 -0.080791 -0.206459 0.082864 0.059575 1.000000